Serge Assaad
I'm currently a research scientist at Waymo, working on the Planner ML team.
Before joining Waymo, I was a Ph.D. student studying deep learning at Duke University.
Through industry experience at Waymo & Amazon, I have designed scalable, fast, accurate, and robust ML models for autonomous driving, trajectory prediction, and biometric recognition. Through my Ph.D. research, I have experience in ML for causal inference and ML for digital pathology
Before my Ph.D. , I completed a B.S. in Biomedical Engineering and Electrical Engineering (Mathematics Minor) at Duke.
Email: sergea [at] google [dot] com
Professional Experience
Papers
S. Assaad, Ph.D. Dissertation: "Principled Deep Learning for Healthcare Applications", 2023 [dissertation][slides]
S. Assaad, D. Dov, R. Davis, S. Kovalsky, W.T. Lee, R. Kahmke, D. Rocke, J. Cohen, R. Henao, L. Carin, D.E. Range, "Thyroid cytopathology cancer diagnosis from smartphone images using machine learning", Modern Pathology, 2023 [paper]
D. Dov, S. Assaad, A. Syedibrahim, J. Bell, J. Huang, J. Madden, R. Bentley, S. McCall, R. Henao, L. Carin, W. Foo, "A hybrid human-machine learning approach to screening prostate biopsies can improve clinical efficiency without compromising diagnostic accuracy", Archives of Pathology & Laboratory Medicine, 2021 [paper]
D. Dov, S.Z. Kovalsky, Q. Feng, S. Assaad, J. Cohen, J. Bell, R. Henao, L. Carin, D.E. Range, ``Use of Machine-Learning-Based Software for the Screening of Thyroid Cytopathology Whole Slide Images'', Archives of Pathology & Laboratory Medicine, 2021 [paper]
D. Dov, S. Assaad, S. Si, R. Wang, H. Xu, S. Kovalsky, J. Bell, D. Range, J. Cohen, R. Henao, L. Carin, "Affinitention Nets: Kernel Perspective on Attention Architectures for Set Classification with Applications to Medical Text and Images", ACM Conference on Health, Inference, and Learning (ACM-CHIL), 2021 [paper][talk]
D. Dov, S.Z. Kovalsky, S. Assaad, J. Cohen, D.E. Range, A.A. Pendse, R. Henao, L. Carin, "Weakly Supervised Instance Learning for Thyroid Malignancy Prediction from Whole Slide Cytopathology Images", Medical Image Analysis, 2020 [paper]
S. Zeng, S. Assaad, C. Tao, S. Datta, L. Carin, F. Li, "Double Robust Representation Learning for Counterfactual Prediction" (preprint) [arXiv]
Professional Services
Reviewer for NeurIPS (2022, Top 10% Reviewer), ICML (2023, 2022), AISTATS (2021), Neural Networks (2021)
Talks/Posters
Research talks
VN-Transformer: Rotation-Equivariant Attention for Vector Neurons, NeurIPS Workshop on ML for Autonomous Driving (2022) [poster]
Rotation-equivariant Transformers for Behavior Prediction, Waymo Research (2021)
Hölder Bounds for Sensitivity Analysis in Causal Reasoning, ICML Workshop on Neglected Assumptions in Causal Inference (2021) [poster]
Quantization and Distillation for Biometric Recognition, Amazon Research (2020)
Counterfactual Representation Learning with Balancing Weights, AISTATS (2021) [poster]
Machine Learning of Continuous Wave Form Doppler Signals, Society for Clinical Vascular Surgery (2018) [link]
Tutorials
Introduction to PyTorch for machine learning (350+ attendees), Duke Winter Breakaway (2021) [link]
Introduction to PyTorch for deep learning, Duke +Data Science (2020) [link]
Crash course in do-calculus for causal inference (2019) [slides]
Image classification with Tensorflow, Duke Datathon (2018)
Keynote speech, Duke International Orientation (2018)
Teaching
Graduate TA Performance & Technology (2021), Vector Space Methods (2020), Data Science Seminar (2019)
Undergraduate TA Calculus (I, II, III) (2015), Linear Algebra (2015), Probability (2015), Data & Decision Sciences (2018), Introduction to Circuits (2017)
Tutor Boeing Grand Challenges Fellow (2018), NCAA Student-Athlete Tutor (2016)